AutoEvoEval: An Automated Framework for Evolving Close-Ended LLM Evaluation Data

TL;DR

AutoEvoEval is an evolution-based framework that systematically generates diverse, challenging test samples for LLM evaluation, revealing significant model sensitivities and highlighting limitations of current benchmarks.

Contribution

We introduce AutoEvoEval, a novel framework with interpretable atomic operations for controlled, multi-step evolution of evaluation data, enhancing robustness analysis of LLMs.

Findings

Atomic operations cause an average accuracy drop of 7.283%.

Structure-disrupting and semantic misleading edits cause the largest declines.

Combining multiple evolution steps amplifies adversarial effects by up to 52.932%.

Abstract

Large language models (LLMs) have shown remarkable performance on various tasks, but existing evaluation benchmarks are often static and insufficient to fully assess their robustness and generalization in realistic scenarios. Prior work using evolutionary or adversarial data augmentation has improved evaluation diversity but lacks systematic control over perturbation types and multi-step complexity, limiting comprehensive robustness analysis. To address these gaps, we propose AutoEvoEval, an evolution-based evaluation framework for close-ended tasks such as multi-choice question answering. AutoEvoEval introduces 22 interpretable atomic evolution operations and supports multi-round compositions, enabling controlled generation of diverse, challenging, and realistic test samples. We conduct extensive experiments addressing four research questions on a broad set of open- and closed-source LLMs. Our results show that atomic operations cause an average accuracy drop of 7.283\%, with structure-disrupting or misleading semantic edits causing the largest declines. Model sensitivities vary significantly for the same perturbation, and combining multiple evolution steps amplifies adversarial effects by up to 52.932\%. These findings suggest current benchmarks may overestimate true model generalization and emphasize the need for evolution-aware robustness evaluation. Code and resources are available at: https://github.com/SYSUSELab/AutoEvoEval.